Bounded Distance Decoding for Random Lattices

Fuente: arXiv
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Main Author: Gao, Shuhong
Format: Preprint
Published: 2025
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author Gao, Shuhong
author_facet Gao, Shuhong
contents The current paper investigates the bounded distance decoding (BDD) problem for ensembles of lattices whose generator matrices have sub-Gaussian entries. We first prove that, for these ensembles the BDD problem is NP-hard in the worst case. Then, we introduce a polynomial-time algorithm based on singular value decomposition (SVD) and establish, both theoretically and through extensive experiments, that, for a random selected lattice from the same ensemble, the algorithm solves the BDD problem with high probability. To the best of our knowledge, this work provides the first example of a lattice problem that is NP-hard in the worst case yet admits a polynomial time algorithm on the average case.
format Preprint
id arxiv_https___arxiv_org_abs_2506_16662
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Bounded Distance Decoding for Random Lattices
Gao, Shuhong
Computational Complexity
68Q25 (primary), 11H06 (secondary)
F.2.2
The current paper investigates the bounded distance decoding (BDD) problem for ensembles of lattices whose generator matrices have sub-Gaussian entries. We first prove that, for these ensembles the BDD problem is NP-hard in the worst case. Then, we introduce a polynomial-time algorithm based on singular value decomposition (SVD) and establish, both theoretically and through extensive experiments, that, for a random selected lattice from the same ensemble, the algorithm solves the BDD problem with high probability. To the best of our knowledge, this work provides the first example of a lattice problem that is NP-hard in the worst case yet admits a polynomial time algorithm on the average case.
title Bounded Distance Decoding for Random Lattices
topic Computational Complexity
68Q25 (primary), 11H06 (secondary)
F.2.2
url https://arxiv.org/abs/2506.16662